Name
Title | ||
|---|---|---|
Deep Learning-Based Phenotypic Assessment of Red Cell Storage Lesions for Safe Transfusions |
Abstract | ||
|---|---|---|
This study presents a novel approach to automatically perform instant phenotypic assessment of red blood cell (RBC) storage lesion in phase images obtained by digital holographic microscopy. The proposed model combines a generative adversarial network (GAN) with marker-controlled watershed segmentation scheme. The GAN model performed RBC segmentations and classifications to develop ageing markers,... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/JBHI.2021.3104650 | IEEE Journal of Biomedical and Health Informatics |
Keywords | DocType | Volume |
Image segmentation,Deep learning,Lesions,Shape,Generative adversarial networks,Feature extraction,Computer architecture | Journal | 26 |
Issue | ISSN | Citations |
3 | 2168-2194 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eunji Kim | 1 | 0 | 0.34 |
| Seonghwan Park | 2 | 0 | 0.34 |
| Seunghyeon Hwang | 3 | 0 | 0.34 |
| Inkyu Moon | 4 | 0 | 0.34 |
| Bahram Javidi | 5 | 110 | 20.30 |